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Record W2732406972 · doi:10.1093/geroni/igx004.1927

INTRODUCING MONTESSORI-BASED VISITING IN A CANADIAN LONG-TERM CARE HOME: RESULTS AND RECOMMENDATIONS

2017· article· en· W2732406972 on OpenAlexaffabout
Paulette V. Hunter, Lilian Thorpe, W. Landen, Laura Pickard, Celine Hounjet

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Methods and Practices
Canadian institutionsUniversity of British ColumbiaUniversity of Saskatchewan
Fundersnot available
KeywordsDementiaPresentation (obstetrics)Unit (ring theory)Montessori methodPsychologyWork (physics)NursingLong-term careMedical educationDay careGerontologyMedicinePedagogyEngineeringMathematics education

Abstract

fetched live from OpenAlex

Enhancing quality of life for residents with advancing dementia remains one of the most significant problems of residential care. Nevertheless, there is growing recognition that when activities are appropriately adapted to individual interests and abilities, residents with dementia can enjoy sustained participation. One method that has seen considerable success in this regard is the Montessori method, introduced by Cameron Camp, and based on the work of Italian physician and educator Maria Montessori (1870 – 1952). We recruited and trained 18 community volunteers to use a Montessori-based approach to visit residents in a secure dementia care unit. In this presentation, we describe the design and implementation of this volunteer visiting program and provide brief data-driven summaries of resident, volunteer, family, and staff input. We offer specific recommendations from our experience to those interested in developing similar Montessori-based initiatives in dementia care settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.443
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

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